MapReduce Implementation of Prestack Kirchhoff Time Migration (PKTM) on Seismic Data

Rizvandi, N; Boloori, A; Kamyabpour, N; Zomaya, A

MapReduce Implementation of Prestack Kirchhoff Time Migration (PKTM) on Seismic Data

Rizvandi, N Boloori, A Kamyabpour, N Zomaya, A

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Publisher:: IEEE
Publication Type:: Conference
Citation:: Rizvandi Nikzad et al. 2011, 'MapReduce Implementation of Prestack Kirchhoff Time Migration (PKTM) on Seismic Data', , IEEE, Piscataway, USA, , pp. 86-91.
Issue Date:: 2011

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Field	Value	Language
dc.contributor.author	Rizvandi, N	en_US
dc.contributor.author	Boloori, A	en_US
dc.contributor.author	Kamyabpour, N	en_US
dc.contributor.author	Zomaya, A	en_US
dc.contributor.editor	NA	en_US
dc.date	20111022	en_US
dc.date.accessioned	2012-10-12T03:36:20Z
dc.date.available	2012-10-12T03:36:20Z
dc.date.issued	2011	en_US
dc.identifier	2011004566	en_US
dc.identifier.citation	Rizvandi Nikzad et al. 2011, 'MapReduce Implementation of Prestack Kirchhoff Time Migration (PKTM) on Seismic Data', , IEEE, Piscataway, USA, , pp. 86-91.	en_US
dc.identifier.issn	978-1-4577-1807-6	en_US
dc.identifier.other	E1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19189
dc.description.abstract	The oil and gas industries have been great consumers of parallel and distributed computing systems, by frequently running technical applications with intensive processing of terabytes of data. By the emergence of cloud computing which gives the opportunity to hire high-throughput computing resources with lower operational costs, such industries have started to adopt their technical applications to be executed on such high-performance commodity systems. In this paper, we first give an overview of forward/inverse Prestack Kirchhoff Time Migration (PKTM) algorithm, as one of the well-known seismic imaging algorithms. Then we will explain our proposed approach to fit this algorithm for running on Google's MapReduce framework. Toward the end, we will analyse the relation between MapReduce-based PKTM completion time and the number of mappers/reducers on pseudo-distributed MapReduce mode.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)	en_US
dc.relation.ispartof	Verified OK	en_US
dc.relation.isbasedon	http://dx.doi.org/10.1109/PDCAT.2011.50	en_US
dc.rights	© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	Algorithm design and analysis , Distributed databases , Earth , Imaging , Industries , Program processors , Signal processing algorithms	en_US
dc.subject.classification	Distributed Computing	en_US
dc.title	MapReduce Implementation of Prestack Kirchhoff Time Migration (PKTM) on Seismic Data	en_US
dc.type	Conference
utslib.location	Piscataway, USA	en_US
utslib.citation.startpage	86	en_US
utslib.citation.endpage	91	en_US
utslib.identifier.org	FEIT.Faculty of Engineering & Information Technology	en_US
utslib.for	080500	en_US
utslib.percentage	100	en_US
dc.location.activity	Gwangju, China	en_US
utslib.copyright.status	closed_access
pubs.declined	1970-01-01T00:00:00.0+1000

Abstract:

The oil and gas industries have been great consumers of parallel and distributed computing systems, by frequently running technical applications with intensive processing of terabytes of data. By the emergence of cloud computing which gives the opportunity to hire high-throughput computing resources with lower operational costs, such industries have started to adopt their technical applications to be executed on such high-performance commodity systems. In this paper, we first give an overview of forward/inverse Prestack Kirchhoff Time Migration (PKTM) algorithm, as one of the well-known seismic imaging algorithms. Then we will explain our proposed approach to fit this algorithm for running on Google's MapReduce framework. Toward the end, we will analyse the relation between MapReduce-based PKTM completion time and the number of mappers/reducers on pseudo-distributed MapReduce mode.

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http://hdl.handle.net/10453/19189